For a logistic regression model, selects a subset of predictor variables that accounts for the variation in the regression model's observation variable.
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| RWLogRegModelSelector () |
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| RWLogRegModelSelector (const RWLogRegModelSelector< F > &rhs) |
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| RWLogRegModelSelector (const RWLogisticRegression ®, RWSearchMethod s) |
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RWLogRegModelSelector< F > & | operator= (const RWLogRegModelSelector< F > &rhs) |
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| RWRegressionModelSelector () |
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| RWRegressionModelSelector (const RWRegressionModelSelector< double, bool, F > &rhs) |
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| RWRegressionModelSelector (const RWRegression< double, bool > ®, RWSearchMethod s) |
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virtual | ~RWRegressionModelSelector () |
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const RWRegressionCalc< double,
bool > & | calcMethod () const |
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double | evalFunctionForSelected () const |
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bool | fail () const |
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RWCString | failMessage () const |
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RWRegressionModelSelector
< double, bool, F > & | operator= (const RWRegressionModelSelector< double, bool, F > &rhs) |
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RWSearchMethod | searchMethod () const |
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const RWBitVec & | selectedParamIndices (RWSearchMethod s) |
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const RWBitVec & | selectedParamIndices () const |
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const RWMathVec< double > & | selectedParamValues () const |
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void | setCalcMethod (const RWRegressionCalc< double, bool > &c) |
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void | setRegression (const RWRegression< double, bool > &r) |
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void | setSearchMethod (RWSearchMethod s) |
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template<class F>
class RWLogRegModelSelector< F >
RWLogRegModelSelector<F> is the class for logistic regression model selection. Provided with a logistic regression model containing a set of candidate predictor variables, the RWLogRegModelSelector class selects a subset of predictor variables that still accounts well for the variation in the regression model's observation variable. The criteria for selecting one predictor subset over another are determined by the template parameter F
. Class F
is a function object that returns a numerical value whenever it is given a regression matrix, an observation vector, and parameter estimates.
To be used as a template parameter for RWLogRegModelSelector, a class F
must implement the following interface:
Synopsis
#include <rw/analytics/gpvalfun.h>
#include <rw/analytics/lgmodsel.h>
#include <rw/analytics/logregress.h>
Example
#include <rw/analytics/lgmodsel.h>
#include <rw/analytics/logregress.h>
#include <rw/analytics/loglevbg.h>
#include <rw/analytics/gpvalfun.h>
#include <iostream>
int main()
{
333 4 654 5 788]";
obsData[0] = obsData[3] = obsData[4] = true;
obsData[1] = obsData[2] = false;
std::cout << "Searching using forward selection---------------"
<< std::endl;
std::cout << "selected parameter indices: "
<< selector.selectedParamIndices() << std::endl;
std::cout << "model criterion value: "
<< selector.evalFunctionForSelected() << std::endl;
std::cout << "Searching using backward selection--------------"
<< std::endl;
std::cout << "selected parameter indices: "
<< selector.selectedParamIndices() << std::endl;
std::cout << "model criterion value: "
<< selector.evalFunctionForSelected() << std::endl;
return 0;
}